Developing an AI application¶
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.
In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories, you can see a few examples below.
The project is broken down into multiple steps:
- Load and preprocess the image dataset
- Train the image classifier on your dataset
- Use the trained classifier to predict image content
We'll lead you through each part which you'll implement in Python.
When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.
First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here.
In [7]:
# Imports here
from torchvision import datasets, transforms, models, utils
import torch
import numpy as np
from torch.autograd import Variable
from torch import nn, optim
from torch.optim import lr_scheduler
from collections import OrderedDict
import os, random, time, argparse
from PIL import Image
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import torch.nn.functional as F
import copy
import json
from workspace_utils import active_session
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
cuda:0
Load the data¶
Here you'll use torchvision to load the data (documentation). The data should be included alongside this notebook, otherwise you can download it here. The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.
The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.
The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's [0.485, 0.456, 0.406] and for the standard deviations [0.229, 0.224, 0.225], calculated from the ImageNet images. These values will shift each color channel to be centered at 0 and range from -1 to 1.
In [8]:
data_dir = 'flowers'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'
use_gpu = torch.cuda.is_available()
print(use_gpu)
True
In [9]:
# constants
CROP_SIZE = 224
RESIZE = 256
MEANS = [0.485, 0.456, 0.406]
ST_DEV = [0.229, 0.224, 0.225]
BATCH_SIZE = 64
# TODO: Define your transforms for the training, validation, and testing sets
data_transforms = {
'training': transforms.Compose([
transforms.RandomRotation(30),
transforms.RandomResizedCrop(CROP_SIZE),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(MEANS, ST_DEV)
]),
'validation': transforms.Compose([
transforms.Resize(RESIZE),
transforms.CenterCrop(CROP_SIZE),
transforms.ToTensor(),
transforms.Normalize(MEANS, ST_DEV)
]),
'testing': transforms.Compose([
transforms.Resize(RESIZE),
transforms.CenterCrop(CROP_SIZE),
transforms.ToTensor(),
transforms.Normalize(MEANS, ST_DEV)
]),
}
# TODO: Load the datasets with ImageFolder
image_datasets = {
'training' : datasets.ImageFolder(train_dir, transform = data_transforms['training']),
'testing' : datasets.ImageFolder(test_dir, transform = data_transforms['testing']),
'validation' : datasets.ImageFolder(valid_dir, transform = data_transforms['validation'])
}
# TODO: Using the image datasets and the trainforms, define the dataloaders
dataloaders = {
'training' : torch.utils.data.DataLoader(image_datasets['training'], batch_size = BATCH_SIZE, shuffle = True),
'testing' : torch.utils.data.DataLoader(image_datasets['testing'], batch_size = BATCH_SIZE, shuffle = True),
'validation' : torch.utils.data.DataLoader(image_datasets['validation'], batch_size = BATCH_SIZE, shuffle = True)
}
In [10]:
dataset_sizes = {x: len(image_datasets[x]) for x in ['training', 'testing', 'validation']}
print(dataset_sizes)
{'training': 6552, 'testing': 819, 'validation': 818}
Label mapping¶
You'll also need to load in a mapping from category label to category name. You can find this in the file cat_to_name.json. It's a JSON object which you can read in with the json module. This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers.
In [11]:
import json
with open('cat_to_name.json', 'r') as f:
cat_to_name = json.load(f)
In [12]:
print(cat_to_name)
{'21': 'fire lily', '3': 'canterbury bells', '45': 'bolero deep blue', '1': 'pink primrose', '34': 'mexican aster', '27': 'prince of wales feathers', '7': 'moon orchid', '16': 'globe-flower', '25': 'grape hyacinth', '26': 'corn poppy', '79': 'toad lily', '39': 'siam tulip', '24': 'red ginger', '67': 'spring crocus', '35': 'alpine sea holly', '32': 'garden phlox', '10': 'globe thistle', '6': 'tiger lily', '93': 'ball moss', '33': 'love in the mist', '9': 'monkshood', '102': 'blackberry lily', '14': 'spear thistle', '19': 'balloon flower', '100': 'blanket flower', '13': 'king protea', '49': 'oxeye daisy', '15': 'yellow iris', '61': 'cautleya spicata', '31': 'carnation', '64': 'silverbush', '68': 'bearded iris', '63': 'black-eyed susan', '69': 'windflower', '62': 'japanese anemone', '20': 'giant white arum lily', '38': 'great masterwort', '4': 'sweet pea', '86': 'tree mallow', '101': 'trumpet creeper', '42': 'daffodil', '22': 'pincushion flower', '2': 'hard-leaved pocket orchid', '54': 'sunflower', '66': 'osteospermum', '70': 'tree poppy', '85': 'desert-rose', '99': 'bromelia', '87': 'magnolia', '5': 'english marigold', '92': 'bee balm', '28': 'stemless gentian', '97': 'mallow', '57': 'gaura', '40': 'lenten rose', '47': 'marigold', '59': 'orange dahlia', '48': 'buttercup', '55': 'pelargonium', '36': 'ruby-lipped cattleya', '91': 'hippeastrum', '29': 'artichoke', '71': 'gazania', '90': 'canna lily', '18': 'peruvian lily', '98': 'mexican petunia', '8': 'bird of paradise', '30': 'sweet william', '17': 'purple coneflower', '52': 'wild pansy', '84': 'columbine', '12': "colt's foot", '11': 'snapdragon', '96': 'camellia', '23': 'fritillary', '50': 'common dandelion', '44': 'poinsettia', '53': 'primula', '72': 'azalea', '65': 'californian poppy', '80': 'anthurium', '76': 'morning glory', '37': 'cape flower', '56': 'bishop of llandaff', '60': 'pink-yellow dahlia', '82': 'clematis', '58': 'geranium', '75': 'thorn apple', '41': 'barbeton daisy', '95': 'bougainvillea', '43': 'sword lily', '83': 'hibiscus', '78': 'lotus lotus', '88': 'cyclamen', '94': 'foxglove', '81': 'frangipani', '74': 'rose', '89': 'watercress', '73': 'water lily', '46': 'wallflower', '77': 'passion flower', '51': 'petunia'}
Building and training the classifier¶
Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from torchvision.models to get the image features. Build and train a new feed-forward classifier using those features.
We're going to leave this part up to you. Refer to the rubric for guidance on successfully completing this section. Things you'll need to do:
- Load a pre-trained network (If you need a starting point, the VGG networks work great and are straightforward to use)
- Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout
- Train the classifier layers using backpropagation using the pre-trained network to get the features
- Track the loss and accuracy on the validation set to determine the best hyperparameters
We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!
When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.
One last important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.
Note for Workspace users: If your network is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. Typically this happens with wide dense layers after the convolutional layers. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with ls -lh), you should reduce the size of your hidden layers and train again.
In [13]:
# TODO: Build and train your network
model = models.densenet121(pretrained=True)
print("Our model: \n\n", model)
print("The state dict keys: \n\n", model.state_dict().keys())
/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.
Our model:
DenseNet(
(features): Sequential(
(conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu0): ReLU(inplace)
(pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(denseblock1): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition1): _Transition(
(norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock2): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition2): _Transition(
(norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock3): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer13): _DenseLayer(
(norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer14): _DenseLayer(
(norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer15): _DenseLayer(
(norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer16): _DenseLayer(
(norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer17): _DenseLayer(
(norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer18): _DenseLayer(
(norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer19): _DenseLayer(
(norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer20): _DenseLayer(
(norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer21): _DenseLayer(
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer22): _DenseLayer(
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer23): _DenseLayer(
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer24): _DenseLayer(
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition3): _Transition(
(norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock4): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer13): _DenseLayer(
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer14): _DenseLayer(
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer15): _DenseLayer(
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer16): _DenseLayer(
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace)
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(classifier): Linear(in_features=1024, out_features=1000, bias=True)
)
The state dict keys:
odict_keys(['features.conv0.weight', 'features.norm0.weight', 'features.norm0.bias', 'features.norm0.running_mean', 'features.norm0.running_var', 'features.denseblock1.denselayer1.norm1.weight', 'features.denseblock1.denselayer1.norm1.bias', 'features.denseblock1.denselayer1.norm1.running_mean', 'features.denseblock1.denselayer1.norm1.running_var', 'features.denseblock1.denselayer1.conv1.weight', 'features.denseblock1.denselayer1.norm2.weight', 'features.denseblock1.denselayer1.norm2.bias', 'features.denseblock1.denselayer1.norm2.running_mean', 'features.denseblock1.denselayer1.norm2.running_var', 'features.denseblock1.denselayer1.conv2.weight', 'features.denseblock1.denselayer2.norm1.weight', 'features.denseblock1.denselayer2.norm1.bias', 'features.denseblock1.denselayer2.norm1.running_mean', 'features.denseblock1.denselayer2.norm1.running_var', 'features.denseblock1.denselayer2.conv1.weight', 'features.denseblock1.denselayer2.norm2.weight', 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'features.denseblock4.denselayer5.norm1.bias', 'features.denseblock4.denselayer5.norm1.running_mean', 'features.denseblock4.denselayer5.norm1.running_var', 'features.denseblock4.denselayer5.conv1.weight', 'features.denseblock4.denselayer5.norm2.weight', 'features.denseblock4.denselayer5.norm2.bias', 'features.denseblock4.denselayer5.norm2.running_mean', 'features.denseblock4.denselayer5.norm2.running_var', 'features.denseblock4.denselayer5.conv2.weight', 'features.denseblock4.denselayer6.norm1.weight', 'features.denseblock4.denselayer6.norm1.bias', 'features.denseblock4.denselayer6.norm1.running_mean', 'features.denseblock4.denselayer6.norm1.running_var', 'features.denseblock4.denselayer6.conv1.weight', 'features.denseblock4.denselayer6.norm2.weight', 'features.denseblock4.denselayer6.norm2.bias', 'features.denseblock4.denselayer6.norm2.running_mean', 'features.denseblock4.denselayer6.norm2.running_var', 'features.denseblock4.denselayer6.conv2.weight', 'features.denseblock4.denselayer7.norm1.weight', 'features.denseblock4.denselayer7.norm1.bias', 'features.denseblock4.denselayer7.norm1.running_mean', 'features.denseblock4.denselayer7.norm1.running_var', 'features.denseblock4.denselayer7.conv1.weight', 'features.denseblock4.denselayer7.norm2.weight', 'features.denseblock4.denselayer7.norm2.bias', 'features.denseblock4.denselayer7.norm2.running_mean', 'features.denseblock4.denselayer7.norm2.running_var', 'features.denseblock4.denselayer7.conv2.weight', 'features.denseblock4.denselayer8.norm1.weight', 'features.denseblock4.denselayer8.norm1.bias', 'features.denseblock4.denselayer8.norm1.running_mean', 'features.denseblock4.denselayer8.norm1.running_var', 'features.denseblock4.denselayer8.conv1.weight', 'features.denseblock4.denselayer8.norm2.weight', 'features.denseblock4.denselayer8.norm2.bias', 'features.denseblock4.denselayer8.norm2.running_mean', 'features.denseblock4.denselayer8.norm2.running_var', 'features.denseblock4.denselayer8.conv2.weight', 'features.denseblock4.denselayer9.norm1.weight', 'features.denseblock4.denselayer9.norm1.bias', 'features.denseblock4.denselayer9.norm1.running_mean', 'features.denseblock4.denselayer9.norm1.running_var', 'features.denseblock4.denselayer9.conv1.weight', 'features.denseblock4.denselayer9.norm2.weight', 'features.denseblock4.denselayer9.norm2.bias', 'features.denseblock4.denselayer9.norm2.running_mean', 'features.denseblock4.denselayer9.norm2.running_var', 'features.denseblock4.denselayer9.conv2.weight', 'features.denseblock4.denselayer10.norm1.weight', 'features.denseblock4.denselayer10.norm1.bias', 'features.denseblock4.denselayer10.norm1.running_mean', 'features.denseblock4.denselayer10.norm1.running_var', 'features.denseblock4.denselayer10.conv1.weight', 'features.denseblock4.denselayer10.norm2.weight', 'features.denseblock4.denselayer10.norm2.bias', 'features.denseblock4.denselayer10.norm2.running_mean', 'features.denseblock4.denselayer10.norm2.running_var', 'features.denseblock4.denselayer10.conv2.weight', 'features.denseblock4.denselayer11.norm1.weight', 'features.denseblock4.denselayer11.norm1.bias', 'features.denseblock4.denselayer11.norm1.running_mean', 'features.denseblock4.denselayer11.norm1.running_var', 'features.denseblock4.denselayer11.conv1.weight', 'features.denseblock4.denselayer11.norm2.weight', 'features.denseblock4.denselayer11.norm2.bias', 'features.denseblock4.denselayer11.norm2.running_mean', 'features.denseblock4.denselayer11.norm2.running_var', 'features.denseblock4.denselayer11.conv2.weight', 'features.denseblock4.denselayer12.norm1.weight', 'features.denseblock4.denselayer12.norm1.bias', 'features.denseblock4.denselayer12.norm1.running_mean', 'features.denseblock4.denselayer12.norm1.running_var', 'features.denseblock4.denselayer12.conv1.weight', 'features.denseblock4.denselayer12.norm2.weight', 'features.denseblock4.denselayer12.norm2.bias', 'features.denseblock4.denselayer12.norm2.running_mean', 'features.denseblock4.denselayer12.norm2.running_var', 'features.denseblock4.denselayer12.conv2.weight', 'features.denseblock4.denselayer13.norm1.weight', 'features.denseblock4.denselayer13.norm1.bias', 'features.denseblock4.denselayer13.norm1.running_mean', 'features.denseblock4.denselayer13.norm1.running_var', 'features.denseblock4.denselayer13.conv1.weight', 'features.denseblock4.denselayer13.norm2.weight', 'features.denseblock4.denselayer13.norm2.bias', 'features.denseblock4.denselayer13.norm2.running_mean', 'features.denseblock4.denselayer13.norm2.running_var', 'features.denseblock4.denselayer13.conv2.weight', 'features.denseblock4.denselayer14.norm1.weight', 'features.denseblock4.denselayer14.norm1.bias', 'features.denseblock4.denselayer14.norm1.running_mean', 'features.denseblock4.denselayer14.norm1.running_var', 'features.denseblock4.denselayer14.conv1.weight', 'features.denseblock4.denselayer14.norm2.weight', 'features.denseblock4.denselayer14.norm2.bias', 'features.denseblock4.denselayer14.norm2.running_mean', 'features.denseblock4.denselayer14.norm2.running_var', 'features.denseblock4.denselayer14.conv2.weight', 'features.denseblock4.denselayer15.norm1.weight', 'features.denseblock4.denselayer15.norm1.bias', 'features.denseblock4.denselayer15.norm1.running_mean', 'features.denseblock4.denselayer15.norm1.running_var', 'features.denseblock4.denselayer15.conv1.weight', 'features.denseblock4.denselayer15.norm2.weight', 'features.denseblock4.denselayer15.norm2.bias', 'features.denseblock4.denselayer15.norm2.running_mean', 'features.denseblock4.denselayer15.norm2.running_var', 'features.denseblock4.denselayer15.conv2.weight', 'features.denseblock4.denselayer16.norm1.weight', 'features.denseblock4.denselayer16.norm1.bias', 'features.denseblock4.denselayer16.norm1.running_mean', 'features.denseblock4.denselayer16.norm1.running_var', 'features.denseblock4.denselayer16.conv1.weight', 'features.denseblock4.denselayer16.norm2.weight', 'features.denseblock4.denselayer16.norm2.bias', 'features.denseblock4.denselayer16.norm2.running_mean', 'features.denseblock4.denselayer16.norm2.running_var', 'features.denseblock4.denselayer16.conv2.weight', 'features.norm5.weight', 'features.norm5.bias', 'features.norm5.running_mean', 'features.norm5.running_var', 'classifier.weight', 'classifier.bias'])
In [25]:
#!pip install torchsummary
#from torchsummary import summary
#summary(model, (3, 224, 224))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 112, 112] 9,408
BatchNorm2d-2 [-1, 64, 112, 112] 128
ReLU-3 [-1, 64, 112, 112] 0
MaxPool2d-4 [-1, 64, 56, 56] 0
BatchNorm2d-5 [-1, 64, 56, 56] 128
ReLU-6 [-1, 64, 56, 56] 0
Conv2d-7 [-1, 128, 56, 56] 8,192
BatchNorm2d-8 [-1, 128, 56, 56] 256
ReLU-9 [-1, 128, 56, 56] 0
Conv2d-10 [-1, 32, 56, 56] 36,864
BatchNorm2d-11 [-1, 96, 56, 56] 192
ReLU-12 [-1, 96, 56, 56] 0
Conv2d-13 [-1, 128, 56, 56] 12,288
BatchNorm2d-14 [-1, 128, 56, 56] 256
ReLU-15 [-1, 128, 56, 56] 0
Conv2d-16 [-1, 32, 56, 56] 36,864
BatchNorm2d-17 [-1, 128, 56, 56] 256
ReLU-18 [-1, 128, 56, 56] 0
Conv2d-19 [-1, 128, 56, 56] 16,384
BatchNorm2d-20 [-1, 128, 56, 56] 256
ReLU-21 [-1, 128, 56, 56] 0
Conv2d-22 [-1, 32, 56, 56] 36,864
BatchNorm2d-23 [-1, 160, 56, 56] 320
ReLU-24 [-1, 160, 56, 56] 0
Conv2d-25 [-1, 128, 56, 56] 20,480
BatchNorm2d-26 [-1, 128, 56, 56] 256
ReLU-27 [-1, 128, 56, 56] 0
Conv2d-28 [-1, 32, 56, 56] 36,864
BatchNorm2d-29 [-1, 192, 56, 56] 384
ReLU-30 [-1, 192, 56, 56] 0
Conv2d-31 [-1, 128, 56, 56] 24,576
BatchNorm2d-32 [-1, 128, 56, 56] 256
ReLU-33 [-1, 128, 56, 56] 0
Conv2d-34 [-1, 32, 56, 56] 36,864
BatchNorm2d-35 [-1, 224, 56, 56] 448
ReLU-36 [-1, 224, 56, 56] 0
Conv2d-37 [-1, 128, 56, 56] 28,672
BatchNorm2d-38 [-1, 128, 56, 56] 256
ReLU-39 [-1, 128, 56, 56] 0
Conv2d-40 [-1, 32, 56, 56] 36,864
BatchNorm2d-41 [-1, 256, 56, 56] 512
ReLU-42 [-1, 256, 56, 56] 0
Conv2d-43 [-1, 128, 56, 56] 32,768
AvgPool2d-44 [-1, 128, 28, 28] 0
BatchNorm2d-45 [-1, 128, 28, 28] 256
ReLU-46 [-1, 128, 28, 28] 0
Conv2d-47 [-1, 128, 28, 28] 16,384
BatchNorm2d-48 [-1, 128, 28, 28] 256
ReLU-49 [-1, 128, 28, 28] 0
Conv2d-50 [-1, 32, 28, 28] 36,864
BatchNorm2d-51 [-1, 160, 28, 28] 320
ReLU-52 [-1, 160, 28, 28] 0
Conv2d-53 [-1, 128, 28, 28] 20,480
BatchNorm2d-54 [-1, 128, 28, 28] 256
ReLU-55 [-1, 128, 28, 28] 0
Conv2d-56 [-1, 32, 28, 28] 36,864
BatchNorm2d-57 [-1, 192, 28, 28] 384
ReLU-58 [-1, 192, 28, 28] 0
Conv2d-59 [-1, 128, 28, 28] 24,576
BatchNorm2d-60 [-1, 128, 28, 28] 256
ReLU-61 [-1, 128, 28, 28] 0
Conv2d-62 [-1, 32, 28, 28] 36,864
BatchNorm2d-63 [-1, 224, 28, 28] 448
ReLU-64 [-1, 224, 28, 28] 0
Conv2d-65 [-1, 128, 28, 28] 28,672
BatchNorm2d-66 [-1, 128, 28, 28] 256
ReLU-67 [-1, 128, 28, 28] 0
Conv2d-68 [-1, 32, 28, 28] 36,864
BatchNorm2d-69 [-1, 256, 28, 28] 512
ReLU-70 [-1, 256, 28, 28] 0
Conv2d-71 [-1, 128, 28, 28] 32,768
BatchNorm2d-72 [-1, 128, 28, 28] 256
ReLU-73 [-1, 128, 28, 28] 0
Conv2d-74 [-1, 32, 28, 28] 36,864
BatchNorm2d-75 [-1, 288, 28, 28] 576
ReLU-76 [-1, 288, 28, 28] 0
Conv2d-77 [-1, 128, 28, 28] 36,864
BatchNorm2d-78 [-1, 128, 28, 28] 256
ReLU-79 [-1, 128, 28, 28] 0
Conv2d-80 [-1, 32, 28, 28] 36,864
BatchNorm2d-81 [-1, 320, 28, 28] 640
ReLU-82 [-1, 320, 28, 28] 0
Conv2d-83 [-1, 128, 28, 28] 40,960
BatchNorm2d-84 [-1, 128, 28, 28] 256
ReLU-85 [-1, 128, 28, 28] 0
Conv2d-86 [-1, 32, 28, 28] 36,864
BatchNorm2d-87 [-1, 352, 28, 28] 704
ReLU-88 [-1, 352, 28, 28] 0
Conv2d-89 [-1, 128, 28, 28] 45,056
BatchNorm2d-90 [-1, 128, 28, 28] 256
ReLU-91 [-1, 128, 28, 28] 0
Conv2d-92 [-1, 32, 28, 28] 36,864
BatchNorm2d-93 [-1, 384, 28, 28] 768
ReLU-94 [-1, 384, 28, 28] 0
Conv2d-95 [-1, 128, 28, 28] 49,152
BatchNorm2d-96 [-1, 128, 28, 28] 256
ReLU-97 [-1, 128, 28, 28] 0
Conv2d-98 [-1, 32, 28, 28] 36,864
BatchNorm2d-99 [-1, 416, 28, 28] 832
ReLU-100 [-1, 416, 28, 28] 0
Conv2d-101 [-1, 128, 28, 28] 53,248
BatchNorm2d-102 [-1, 128, 28, 28] 256
ReLU-103 [-1, 128, 28, 28] 0
Conv2d-104 [-1, 32, 28, 28] 36,864
BatchNorm2d-105 [-1, 448, 28, 28] 896
ReLU-106 [-1, 448, 28, 28] 0
Conv2d-107 [-1, 128, 28, 28] 57,344
BatchNorm2d-108 [-1, 128, 28, 28] 256
ReLU-109 [-1, 128, 28, 28] 0
Conv2d-110 [-1, 32, 28, 28] 36,864
BatchNorm2d-111 [-1, 480, 28, 28] 960
ReLU-112 [-1, 480, 28, 28] 0
Conv2d-113 [-1, 128, 28, 28] 61,440
BatchNorm2d-114 [-1, 128, 28, 28] 256
ReLU-115 [-1, 128, 28, 28] 0
Conv2d-116 [-1, 32, 28, 28] 36,864
BatchNorm2d-117 [-1, 512, 28, 28] 1,024
ReLU-118 [-1, 512, 28, 28] 0
Conv2d-119 [-1, 256, 28, 28] 131,072
AvgPool2d-120 [-1, 256, 14, 14] 0
BatchNorm2d-121 [-1, 256, 14, 14] 512
ReLU-122 [-1, 256, 14, 14] 0
Conv2d-123 [-1, 128, 14, 14] 32,768
BatchNorm2d-124 [-1, 128, 14, 14] 256
ReLU-125 [-1, 128, 14, 14] 0
Conv2d-126 [-1, 32, 14, 14] 36,864
BatchNorm2d-127 [-1, 288, 14, 14] 576
ReLU-128 [-1, 288, 14, 14] 0
Conv2d-129 [-1, 128, 14, 14] 36,864
BatchNorm2d-130 [-1, 128, 14, 14] 256
ReLU-131 [-1, 128, 14, 14] 0
Conv2d-132 [-1, 32, 14, 14] 36,864
BatchNorm2d-133 [-1, 320, 14, 14] 640
ReLU-134 [-1, 320, 14, 14] 0
Conv2d-135 [-1, 128, 14, 14] 40,960
BatchNorm2d-136 [-1, 128, 14, 14] 256
ReLU-137 [-1, 128, 14, 14] 0
Conv2d-138 [-1, 32, 14, 14] 36,864
BatchNorm2d-139 [-1, 352, 14, 14] 704
ReLU-140 [-1, 352, 14, 14] 0
Conv2d-141 [-1, 128, 14, 14] 45,056
BatchNorm2d-142 [-1, 128, 14, 14] 256
ReLU-143 [-1, 128, 14, 14] 0
Conv2d-144 [-1, 32, 14, 14] 36,864
BatchNorm2d-145 [-1, 384, 14, 14] 768
ReLU-146 [-1, 384, 14, 14] 0
Conv2d-147 [-1, 128, 14, 14] 49,152
BatchNorm2d-148 [-1, 128, 14, 14] 256
ReLU-149 [-1, 128, 14, 14] 0
Conv2d-150 [-1, 32, 14, 14] 36,864
BatchNorm2d-151 [-1, 416, 14, 14] 832
ReLU-152 [-1, 416, 14, 14] 0
Conv2d-153 [-1, 128, 14, 14] 53,248
BatchNorm2d-154 [-1, 128, 14, 14] 256
ReLU-155 [-1, 128, 14, 14] 0
Conv2d-156 [-1, 32, 14, 14] 36,864
BatchNorm2d-157 [-1, 448, 14, 14] 896
ReLU-158 [-1, 448, 14, 14] 0
Conv2d-159 [-1, 128, 14, 14] 57,344
BatchNorm2d-160 [-1, 128, 14, 14] 256
ReLU-161 [-1, 128, 14, 14] 0
Conv2d-162 [-1, 32, 14, 14] 36,864
BatchNorm2d-163 [-1, 480, 14, 14] 960
ReLU-164 [-1, 480, 14, 14] 0
Conv2d-165 [-1, 128, 14, 14] 61,440
BatchNorm2d-166 [-1, 128, 14, 14] 256
ReLU-167 [-1, 128, 14, 14] 0
Conv2d-168 [-1, 32, 14, 14] 36,864
BatchNorm2d-169 [-1, 512, 14, 14] 1,024
ReLU-170 [-1, 512, 14, 14] 0
Conv2d-171 [-1, 128, 14, 14] 65,536
BatchNorm2d-172 [-1, 128, 14, 14] 256
ReLU-173 [-1, 128, 14, 14] 0
Conv2d-174 [-1, 32, 14, 14] 36,864
BatchNorm2d-175 [-1, 544, 14, 14] 1,088
ReLU-176 [-1, 544, 14, 14] 0
Conv2d-177 [-1, 128, 14, 14] 69,632
BatchNorm2d-178 [-1, 128, 14, 14] 256
ReLU-179 [-1, 128, 14, 14] 0
Conv2d-180 [-1, 32, 14, 14] 36,864
BatchNorm2d-181 [-1, 576, 14, 14] 1,152
ReLU-182 [-1, 576, 14, 14] 0
Conv2d-183 [-1, 128, 14, 14] 73,728
BatchNorm2d-184 [-1, 128, 14, 14] 256
ReLU-185 [-1, 128, 14, 14] 0
Conv2d-186 [-1, 32, 14, 14] 36,864
BatchNorm2d-187 [-1, 608, 14, 14] 1,216
ReLU-188 [-1, 608, 14, 14] 0
Conv2d-189 [-1, 128, 14, 14] 77,824
BatchNorm2d-190 [-1, 128, 14, 14] 256
ReLU-191 [-1, 128, 14, 14] 0
Conv2d-192 [-1, 32, 14, 14] 36,864
BatchNorm2d-193 [-1, 640, 14, 14] 1,280
ReLU-194 [-1, 640, 14, 14] 0
Conv2d-195 [-1, 128, 14, 14] 81,920
BatchNorm2d-196 [-1, 128, 14, 14] 256
ReLU-197 [-1, 128, 14, 14] 0
Conv2d-198 [-1, 32, 14, 14] 36,864
BatchNorm2d-199 [-1, 672, 14, 14] 1,344
ReLU-200 [-1, 672, 14, 14] 0
Conv2d-201 [-1, 128, 14, 14] 86,016
BatchNorm2d-202 [-1, 128, 14, 14] 256
ReLU-203 [-1, 128, 14, 14] 0
Conv2d-204 [-1, 32, 14, 14] 36,864
BatchNorm2d-205 [-1, 704, 14, 14] 1,408
ReLU-206 [-1, 704, 14, 14] 0
Conv2d-207 [-1, 128, 14, 14] 90,112
BatchNorm2d-208 [-1, 128, 14, 14] 256
ReLU-209 [-1, 128, 14, 14] 0
Conv2d-210 [-1, 32, 14, 14] 36,864
BatchNorm2d-211 [-1, 736, 14, 14] 1,472
ReLU-212 [-1, 736, 14, 14] 0
Conv2d-213 [-1, 128, 14, 14] 94,208
BatchNorm2d-214 [-1, 128, 14, 14] 256
ReLU-215 [-1, 128, 14, 14] 0
Conv2d-216 [-1, 32, 14, 14] 36,864
BatchNorm2d-217 [-1, 768, 14, 14] 1,536
ReLU-218 [-1, 768, 14, 14] 0
Conv2d-219 [-1, 128, 14, 14] 98,304
BatchNorm2d-220 [-1, 128, 14, 14] 256
ReLU-221 [-1, 128, 14, 14] 0
Conv2d-222 [-1, 32, 14, 14] 36,864
BatchNorm2d-223 [-1, 800, 14, 14] 1,600
ReLU-224 [-1, 800, 14, 14] 0
Conv2d-225 [-1, 128, 14, 14] 102,400
BatchNorm2d-226 [-1, 128, 14, 14] 256
ReLU-227 [-1, 128, 14, 14] 0
Conv2d-228 [-1, 32, 14, 14] 36,864
BatchNorm2d-229 [-1, 832, 14, 14] 1,664
ReLU-230 [-1, 832, 14, 14] 0
Conv2d-231 [-1, 128, 14, 14] 106,496
BatchNorm2d-232 [-1, 128, 14, 14] 256
ReLU-233 [-1, 128, 14, 14] 0
Conv2d-234 [-1, 32, 14, 14] 36,864
BatchNorm2d-235 [-1, 864, 14, 14] 1,728
ReLU-236 [-1, 864, 14, 14] 0
Conv2d-237 [-1, 128, 14, 14] 110,592
BatchNorm2d-238 [-1, 128, 14, 14] 256
ReLU-239 [-1, 128, 14, 14] 0
Conv2d-240 [-1, 32, 14, 14] 36,864
BatchNorm2d-241 [-1, 896, 14, 14] 1,792
ReLU-242 [-1, 896, 14, 14] 0
Conv2d-243 [-1, 128, 14, 14] 114,688
BatchNorm2d-244 [-1, 128, 14, 14] 256
ReLU-245 [-1, 128, 14, 14] 0
Conv2d-246 [-1, 32, 14, 14] 36,864
BatchNorm2d-247 [-1, 928, 14, 14] 1,856
ReLU-248 [-1, 928, 14, 14] 0
Conv2d-249 [-1, 128, 14, 14] 118,784
BatchNorm2d-250 [-1, 128, 14, 14] 256
ReLU-251 [-1, 128, 14, 14] 0
Conv2d-252 [-1, 32, 14, 14] 36,864
BatchNorm2d-253 [-1, 960, 14, 14] 1,920
ReLU-254 [-1, 960, 14, 14] 0
Conv2d-255 [-1, 128, 14, 14] 122,880
BatchNorm2d-256 [-1, 128, 14, 14] 256
ReLU-257 [-1, 128, 14, 14] 0
Conv2d-258 [-1, 32, 14, 14] 36,864
BatchNorm2d-259 [-1, 992, 14, 14] 1,984
ReLU-260 [-1, 992, 14, 14] 0
Conv2d-261 [-1, 128, 14, 14] 126,976
BatchNorm2d-262 [-1, 128, 14, 14] 256
ReLU-263 [-1, 128, 14, 14] 0
Conv2d-264 [-1, 32, 14, 14] 36,864
BatchNorm2d-265 [-1, 1024, 14, 14] 2,048
ReLU-266 [-1, 1024, 14, 14] 0
Conv2d-267 [-1, 512, 14, 14] 524,288
AvgPool2d-268 [-1, 512, 7, 7] 0
BatchNorm2d-269 [-1, 512, 7, 7] 1,024
ReLU-270 [-1, 512, 7, 7] 0
Conv2d-271 [-1, 128, 7, 7] 65,536
BatchNorm2d-272 [-1, 128, 7, 7] 256
ReLU-273 [-1, 128, 7, 7] 0
Conv2d-274 [-1, 32, 7, 7] 36,864
BatchNorm2d-275 [-1, 544, 7, 7] 1,088
ReLU-276 [-1, 544, 7, 7] 0
Conv2d-277 [-1, 128, 7, 7] 69,632
BatchNorm2d-278 [-1, 128, 7, 7] 256
ReLU-279 [-1, 128, 7, 7] 0
Conv2d-280 [-1, 32, 7, 7] 36,864
BatchNorm2d-281 [-1, 576, 7, 7] 1,152
ReLU-282 [-1, 576, 7, 7] 0
Conv2d-283 [-1, 128, 7, 7] 73,728
BatchNorm2d-284 [-1, 128, 7, 7] 256
ReLU-285 [-1, 128, 7, 7] 0
Conv2d-286 [-1, 32, 7, 7] 36,864
BatchNorm2d-287 [-1, 608, 7, 7] 1,216
ReLU-288 [-1, 608, 7, 7] 0
Conv2d-289 [-1, 128, 7, 7] 77,824
BatchNorm2d-290 [-1, 128, 7, 7] 256
ReLU-291 [-1, 128, 7, 7] 0
Conv2d-292 [-1, 32, 7, 7] 36,864
BatchNorm2d-293 [-1, 640, 7, 7] 1,280
ReLU-294 [-1, 640, 7, 7] 0
Conv2d-295 [-1, 128, 7, 7] 81,920
BatchNorm2d-296 [-1, 128, 7, 7] 256
ReLU-297 [-1, 128, 7, 7] 0
Conv2d-298 [-1, 32, 7, 7] 36,864
BatchNorm2d-299 [-1, 672, 7, 7] 1,344
ReLU-300 [-1, 672, 7, 7] 0
Conv2d-301 [-1, 128, 7, 7] 86,016
BatchNorm2d-302 [-1, 128, 7, 7] 256
ReLU-303 [-1, 128, 7, 7] 0
Conv2d-304 [-1, 32, 7, 7] 36,864
BatchNorm2d-305 [-1, 704, 7, 7] 1,408
ReLU-306 [-1, 704, 7, 7] 0
Conv2d-307 [-1, 128, 7, 7] 90,112
BatchNorm2d-308 [-1, 128, 7, 7] 256
ReLU-309 [-1, 128, 7, 7] 0
Conv2d-310 [-1, 32, 7, 7] 36,864
BatchNorm2d-311 [-1, 736, 7, 7] 1,472
ReLU-312 [-1, 736, 7, 7] 0
Conv2d-313 [-1, 128, 7, 7] 94,208
BatchNorm2d-314 [-1, 128, 7, 7] 256
ReLU-315 [-1, 128, 7, 7] 0
Conv2d-316 [-1, 32, 7, 7] 36,864
BatchNorm2d-317 [-1, 768, 7, 7] 1,536
ReLU-318 [-1, 768, 7, 7] 0
Conv2d-319 [-1, 128, 7, 7] 98,304
BatchNorm2d-320 [-1, 128, 7, 7] 256
ReLU-321 [-1, 128, 7, 7] 0
Conv2d-322 [-1, 32, 7, 7] 36,864
BatchNorm2d-323 [-1, 800, 7, 7] 1,600
ReLU-324 [-1, 800, 7, 7] 0
Conv2d-325 [-1, 128, 7, 7] 102,400
BatchNorm2d-326 [-1, 128, 7, 7] 256
ReLU-327 [-1, 128, 7, 7] 0
Conv2d-328 [-1, 32, 7, 7] 36,864
BatchNorm2d-329 [-1, 832, 7, 7] 1,664
ReLU-330 [-1, 832, 7, 7] 0
Conv2d-331 [-1, 128, 7, 7] 106,496
BatchNorm2d-332 [-1, 128, 7, 7] 256
ReLU-333 [-1, 128, 7, 7] 0
Conv2d-334 [-1, 32, 7, 7] 36,864
BatchNorm2d-335 [-1, 864, 7, 7] 1,728
ReLU-336 [-1, 864, 7, 7] 0
Conv2d-337 [-1, 128, 7, 7] 110,592
BatchNorm2d-338 [-1, 128, 7, 7] 256
ReLU-339 [-1, 128, 7, 7] 0
Conv2d-340 [-1, 32, 7, 7] 36,864
BatchNorm2d-341 [-1, 896, 7, 7] 1,792
ReLU-342 [-1, 896, 7, 7] 0
Conv2d-343 [-1, 128, 7, 7] 114,688
BatchNorm2d-344 [-1, 128, 7, 7] 256
ReLU-345 [-1, 128, 7, 7] 0
Conv2d-346 [-1, 32, 7, 7] 36,864
BatchNorm2d-347 [-1, 928, 7, 7] 1,856
ReLU-348 [-1, 928, 7, 7] 0
Conv2d-349 [-1, 128, 7, 7] 118,784
BatchNorm2d-350 [-1, 128, 7, 7] 256
ReLU-351 [-1, 128, 7, 7] 0
Conv2d-352 [-1, 32, 7, 7] 36,864
BatchNorm2d-353 [-1, 960, 7, 7] 1,920
ReLU-354 [-1, 960, 7, 7] 0
Conv2d-355 [-1, 128, 7, 7] 122,880
BatchNorm2d-356 [-1, 128, 7, 7] 256
ReLU-357 [-1, 128, 7, 7] 0
Conv2d-358 [-1, 32, 7, 7] 36,864
BatchNorm2d-359 [-1, 992, 7, 7] 1,984
ReLU-360 [-1, 992, 7, 7] 0
Conv2d-361 [-1, 128, 7, 7] 126,976
BatchNorm2d-362 [-1, 128, 7, 7] 256
ReLU-363 [-1, 128, 7, 7] 0
Conv2d-364 [-1, 32, 7, 7] 36,864
BatchNorm2d-365 [-1, 1024, 7, 7] 2,048
Linear-366 [-1, 1000] 1,025,000
================================================================
Total params: 7,978,856
Trainable params: 7,978,856
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 294.20
Params size (MB): 30.44
Estimated Total Size (MB): 325.21
----------------------------------------------------------------
In [14]:
# Freeze parameters so we don't backprop through them
for param in model.parameters():
param.requires_grad = False
'''classifier = nn.Sequential(OrderedDict([
('dropout',nn.Dropout(0.3)),
('inputs', nn.Linear(1024, 512)),
('relu1', nn.ReLU()),
('hidden_layer1', nn.Linear(512, 256)),
('relu2',nn.ReLU()),
('hidden_layer2',nn.Linear(256,128)),
('relu3',nn.ReLU()),
('hidden_layer3',nn.Linear(128,102)),
('output', nn.LogSoftmax(dim=1))
]))
model.classifier = classifier'''
LEARNING_RATE = 0.001
def get_model(structure='densenet121', dropout=0.3, hidden_layer1 = 512, lr = LEARNING_RATE):
if structure == 'vgg16':
model = models.vgg16(pretrained = True)
elif structure == 'densenet121':
model = models.densenet121(pretrained = True)
elif structure == 'alexnet':
model = models.alexnet(pretrained = True)
else:
print("The model is not expected".format(structure))
for param in model.parameters():
param.requires_grad = False
classifier = nn.Sequential(OrderedDict([
('dropout',nn.Dropout(dropout)),
('inputs', nn.Linear(1024, hidden_layer1)),
('relu1', nn.ReLU()),
('hidden_layer1', nn.Linear(hidden_layer1, 256)),
('relu2',nn.ReLU()),
('hidden_layer2',nn.Linear(256, 128)),
('relu3',nn.ReLU()),
('hidden_layer3',nn.Linear(128, 102)),
('output', nn.LogSoftmax(dim=1))
]))
model.classifier = classifier
model.cuda()
return model
In [15]:
# Training
def training(model, criterion, optimizer, training_loader, validation_loader, epochs = 10):
# Putting the model into training mode
model.train()
print_every = 10
steps = 0
# Iterates through each training pass based on #epochs & GPU/CPU
for epoch in range(epochs):
running_loss = 0
for inputs, labels in iter(training_loader):
steps += 1
inputs = Variable(inputs.float().cuda())
labels = Variable(labels.long().cuda())
# Forward and backward passes
optimizer.zero_grad()
output = model.forward(inputs) # Forward propogation
loss = criterion(output, labels) # Calculates loss
loss.backward() # Calcule the gradient
optimizer.step() # Update the weights based on the gradient and the learning rate
running_loss += loss.item()
if steps % print_every == 0:
validation_loss, accuracy = validating(model, criterion, validation_loader)
print("Epoch: {}/{} ".format(epoch + 1, epochs),
"Training Loss: {:.3f} ".format(running_loss/print_every),
"Validation Loss: {:.3f} ".format(validation_loss),
"Validation Accuracy: {:.3f}".format(accuracy))
In [16]:
def validating(model, criterion, data_loader):
test_loss = 0
accuracy = 0
# Set validation mode to the model
model.eval()
with torch.no_grad():
for inputs, labels in data_loader:
inputs = Variable(inputs.float().cuda())
labels = Variable(labels.long().cuda())
output = model.forward(inputs)
test_loss += criterion(output, labels).item()
ps = torch.exp(output).data
equality = (labels.data == ps.max(1)[1])
accuracy += equality.type_as(torch.FloatTensor()).mean()
return test_loss/len(data_loader), accuracy/len(data_loader)
In [17]:
epochs = 6
if use_gpu:
print ("Using GPU: "+ str(use_gpu))
model = model.cuda()
Using GPU: True
In [18]:
criterion = nn.CrossEntropyLoss() #nn.NLLLoss()
model = get_model('densenet121')
optimizer = optim.Adam(model.classifier.parameters(), LEARNING_RATE)
/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.
In [19]:
with active_session():
training(model, criterion, optimizer, dataloaders['training'], dataloaders['validation'], epochs)
Epoch: 1/6 Training Loss: 4.593 Validation Loss: 4.531 Validation Accuracy: 0.022 Epoch: 1/6 Training Loss: 9.137 Validation Loss: 4.460 Validation Accuracy: 0.034 Epoch: 1/6 Training Loss: 13.535 Validation Loss: 4.279 Validation Accuracy: 0.076 Epoch: 1/6 Training Loss: 17.717 Validation Loss: 4.032 Validation Accuracy: 0.130 Epoch: 1/6 Training Loss: 21.750 Validation Loss: 3.739 Validation Accuracy: 0.147 Epoch: 1/6 Training Loss: 25.399 Validation Loss: 3.470 Validation Accuracy: 0.201 Epoch: 1/6 Training Loss: 28.838 Validation Loss: 3.170 Validation Accuracy: 0.267 Epoch: 1/6 Training Loss: 32.013 Validation Loss: 2.821 Validation Accuracy: 0.299 Epoch: 1/6 Training Loss: 34.976 Validation Loss: 2.529 Validation Accuracy: 0.365 Epoch: 1/6 Training Loss: 37.663 Validation Loss: 2.357 Validation Accuracy: 0.394 Epoch: 2/6 Training Loss: 1.638 Validation Loss: 2.174 Validation Accuracy: 0.399 Epoch: 2/6 Training Loss: 3.818 Validation Loss: 1.927 Validation Accuracy: 0.480 Epoch: 2/6 Training Loss: 5.826 Validation Loss: 1.684 Validation Accuracy: 0.566 Epoch: 2/6 Training Loss: 7.731 Validation Loss: 1.541 Validation Accuracy: 0.586 Epoch: 2/6 Training Loss: 9.522 Validation Loss: 1.513 Validation Accuracy: 0.580 Epoch: 2/6 Training Loss: 11.337 Validation Loss: 1.597 Validation Accuracy: 0.548 Epoch: 2/6 Training Loss: 13.104 Validation Loss: 1.413 Validation Accuracy: 0.614 Epoch: 2/6 Training Loss: 14.733 Validation Loss: 1.300 Validation Accuracy: 0.621 Epoch: 2/6 Training Loss: 16.329 Validation Loss: 1.453 Validation Accuracy: 0.580 Epoch: 2/6 Training Loss: 17.821 Validation Loss: 1.206 Validation Accuracy: 0.663 Epoch: 3/6 Training Loss: 0.587 Validation Loss: 1.154 Validation Accuracy: 0.689 Epoch: 3/6 Training Loss: 1.822 Validation Loss: 1.155 Validation Accuracy: 0.684 Epoch: 3/6 Training Loss: 3.087 Validation Loss: 1.115 Validation Accuracy: 0.661 Epoch: 3/6 Training Loss: 4.391 Validation Loss: 1.147 Validation Accuracy: 0.670 Epoch: 3/6 Training Loss: 5.607 Validation Loss: 1.014 Validation Accuracy: 0.709 Epoch: 3/6 Training Loss: 6.682 Validation Loss: 1.030 Validation Accuracy: 0.694 Epoch: 3/6 Training Loss: 7.764 Validation Loss: 0.846 Validation Accuracy: 0.776 Epoch: 3/6 Training Loss: 8.808 Validation Loss: 0.858 Validation Accuracy: 0.755 Epoch: 3/6 Training Loss: 9.980 Validation Loss: 0.948 Validation Accuracy: 0.734 Epoch: 3/6 Training Loss: 11.083 Validation Loss: 0.889 Validation Accuracy: 0.734 Epoch: 4/6 Training Loss: 0.069 Validation Loss: 0.842 Validation Accuracy: 0.752 Epoch: 4/6 Training Loss: 1.071 Validation Loss: 0.792 Validation Accuracy: 0.773 Epoch: 4/6 Training Loss: 1.964 Validation Loss: 0.775 Validation Accuracy: 0.768 Epoch: 4/6 Training Loss: 2.942 Validation Loss: 0.752 Validation Accuracy: 0.786 Epoch: 4/6 Training Loss: 3.844 Validation Loss: 0.834 Validation Accuracy: 0.777 Epoch: 4/6 Training Loss: 4.822 Validation Loss: 0.764 Validation Accuracy: 0.789 Epoch: 4/6 Training Loss: 5.861 Validation Loss: 0.701 Validation Accuracy: 0.793 Epoch: 4/6 Training Loss: 6.774 Validation Loss: 0.751 Validation Accuracy: 0.797 Epoch: 4/6 Training Loss: 7.635 Validation Loss: 0.650 Validation Accuracy: 0.825 Epoch: 4/6 Training Loss: 8.404 Validation Loss: 0.712 Validation Accuracy: 0.793 Epoch: 4/6 Training Loss: 9.193 Validation Loss: 0.723 Validation Accuracy: 0.790 Epoch: 5/6 Training Loss: 0.587 Validation Loss: 0.692 Validation Accuracy: 0.800 Epoch: 5/6 Training Loss: 1.371 Validation Loss: 0.685 Validation Accuracy: 0.795 Epoch: 5/6 Training Loss: 2.170 Validation Loss: 0.671 Validation Accuracy: 0.808 Epoch: 5/6 Training Loss: 3.025 Validation Loss: 0.614 Validation Accuracy: 0.844 Epoch: 5/6 Training Loss: 3.764 Validation Loss: 0.681 Validation Accuracy: 0.808 Epoch: 5/6 Training Loss: 4.482 Validation Loss: 0.594 Validation Accuracy: 0.824 Epoch: 5/6 Training Loss: 5.192 Validation Loss: 0.574 Validation Accuracy: 0.835 Epoch: 5/6 Training Loss: 5.979 Validation Loss: 0.556 Validation Accuracy: 0.838 Epoch: 5/6 Training Loss: 6.741 Validation Loss: 0.571 Validation Accuracy: 0.827 Epoch: 5/6 Training Loss: 7.459 Validation Loss: 0.656 Validation Accuracy: 0.817 Epoch: 6/6 Training Loss: 0.362 Validation Loss: 0.645 Validation Accuracy: 0.834 Epoch: 6/6 Training Loss: 1.088 Validation Loss: 0.641 Validation Accuracy: 0.827 Epoch: 6/6 Training Loss: 1.776 Validation Loss: 0.571 Validation Accuracy: 0.844 Epoch: 6/6 Training Loss: 2.510 Validation Loss: 0.546 Validation Accuracy: 0.858 Epoch: 6/6 Training Loss: 3.143 Validation Loss: 0.563 Validation Accuracy: 0.839 Epoch: 6/6 Training Loss: 3.884 Validation Loss: 0.522 Validation Accuracy: 0.851 Epoch: 6/6 Training Loss: 4.595 Validation Loss: 0.530 Validation Accuracy: 0.856 Epoch: 6/6 Training Loss: 5.225 Validation Loss: 0.545 Validation Accuracy: 0.835 Epoch: 6/6 Training Loss: 5.975 Validation Loss: 0.503 Validation Accuracy: 0.857 Epoch: 6/6 Training Loss: 6.543 Validation Loss: 0.530 Validation Accuracy: 0.855
Testing your network¶
It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well.
In [20]:
# TODO: Do validation on the test set
validation_criterion = nn.NLLLoss()
validating(model, validation_criterion, dataloaders['validation'])
Out[20]:
(0.6214943642799671, tensor(0.8278))
Save the checkpoint¶
Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: image_datasets['train'].class_to_idx. You can attach this to the model as an attribute which makes inference easier later on.
model.class_to_idx = image_datasets['train'].class_to_idx
Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, optimizer.state_dict. You'll likely want to use this trained model in the next part of the project, so best to save it now.
In [22]:
# TODO: Save the checkpoint
model.class_to_idx = image_datasets['training'].class_to_idx
checkpoint = {'epochs': epochs,
'batch_size': BATCH_SIZE,
'classifier': model.classifier,
'structure' : 'densenet121',
#'hidden_layer1': 512,
'optimizer': optimizer.state_dict(),
'state_dict': model.state_dict(), # Holds all the weights and biases
'class_to_idx': model.class_to_idx
}
torch.save(checkpoint, 'checkpoint.pth')
In [23]:
print(model.state_dict())
OrderedDict([('features.conv0.weight', tensor([[[[ 7.8276e-02, 1.4949e-01, 1.6611e-01, ..., 1.7676e-01,
1.6588e-01, 1.4101e-01],
[ 1.7546e-01, 2.4408e-01, 2.5000e-01, ..., 2.7452e-01,
2.5245e-01, 2.2199e-01],
[ 1.2331e-01, 1.6441e-01, 1.4922e-01, ..., 1.6301e-01,
1.6191e-01, 1.4061e-01],
...,
[-1.0461e-01, -1.2065e-01, -1.1969e-01, ..., -1.1355e-01,
-1.1181e-01, -1.1653e-01],
[-1.4747e-01, -1.8658e-01, -1.8272e-01, ..., -2.1694e-01,
-2.0213e-01, -1.8302e-01],
[-2.0729e-01, -2.7118e-01, -2.8157e-01, ..., -2.8711e-01,
-2.4883e-01, -2.2605e-01]],
[[ 1.6418e-01, 2.4814e-01, 2.6538e-01, ..., 2.7358e-01,
2.5693e-01, 2.2483e-01],
[ 2.4226e-01, 3.2158e-01, 3.2346e-01, ..., 3.4569e-01,
3.1805e-01, 2.8128e-01],
[ 1.5825e-01, 2.0253e-01, 1.8364e-01, ..., 1.9484e-01,
1.8966e-01, 1.6964e-01],
...,
[-1.2547e-01, -1.8073e-01, -1.8958e-01, ..., -1.7603e-01,
-1.7623e-01, -1.7415e-01],
[-1.8183e-01, -2.6247e-01, -2.6084e-01, ..., -2.9753e-01,
-2.7600e-01, -2.4196e-01],
[-2.4014e-01, -3.3787e-01, -3.6857e-01, ..., -3.6870e-01,
-3.1372e-01, -2.7300e-01]],
[[ 4.3790e-02, 1.1260e-01, 1.2304e-01, ..., 1.1773e-01,
1.1248e-01, 9.2860e-02],
[ 1.3078e-01, 2.0256e-01, 1.8976e-01, ..., 2.0730e-01,
1.9033e-01, 1.6014e-01],
[ 8.7673e-02, 1.3089e-01, 1.0913e-01, ..., 1.1802e-01,
1.0854e-01, 9.1808e-02],
...,
[-8.0166e-02, -1.2586e-01, -1.1267e-01, ..., -1.0376e-01,
-1.0435e-01, -1.1052e-01],
[-7.5893e-02, -1.3055e-01, -1.2621e-01, ..., -1.5039e-01,
-1.4016e-01, -1.1602e-01],
[-1.1338e-01, -1.7756e-01, -1.9754e-01, ..., -1.9402e-01,
-1.6118e-01, -1.2721e-01]]],
[[[ 1.4191e-02, 2.0846e-02, 2.0331e-02, ..., -9.0996e-03,
-1.4352e-02, -2.7588e-03],
[ 5.4299e-03, -2.4951e-03, -5.6107e-03, ..., -4.2277e-02,
-6.1752e-04, -1.6583e-03],
[-2.1288e-02, -4.8074e-02, -6.1015e-02, ..., 7.7539e-02,
8.0279e-02, 5.2814e-02],
...,
[ 9.7766e-03, 1.9108e-02, -1.5814e-02, ..., -4.4661e-01,
-3.4646e-01, -1.5702e-01],
[-2.7226e-02, -1.4129e-01, -2.3819e-01, ..., 2.0699e-02,
1.3012e-01, 7.2974e-02],
[-2.3009e-02, 3.8790e-02, 1.3627e-01, ..., 9.8952e-02,
3.7285e-02, 1.3395e-02]],
[[-7.7194e-03, 2.1168e-03, -1.5517e-03, ..., -1.2765e-02,
-5.6384e-03, 1.8512e-02],
[-4.2461e-03, 6.3333e-04, -2.7641e-02, ..., -4.6375e-02,
7.2856e-03, 2.7867e-02],
[-1.7378e-02, -4.5333e-02, -4.8481e-02, ..., 1.4805e-01,
1.3967e-01, 8.4136e-02],
...,
[ 2.8083e-02, 4.5874e-02, -2.1670e-02, ..., -6.1030e-01,
-4.7836e-01, -2.0833e-01],
[-5.2253e-02, -2.0379e-01, -3.4409e-01, ..., -1.6203e-02,
1.6814e-01, 9.1657e-02],
[-2.4587e-02, 2.3830e-02, 1.4674e-01, ..., 1.5153e-01,
7.5217e-02, 1.0261e-02]],
[[-7.8859e-03, 6.9697e-03, -3.9297e-03, ..., 1.2928e-02,
-1.0919e-02, -1.0605e-02],
[ 5.7105e-03, 2.0495e-02, 3.0640e-03, ..., -7.1212e-03,
-8.9823e-03, 2.4318e-03],
[-4.0016e-03, -4.9766e-02, -6.8471e-02, ..., 2.2613e-03,
1.0112e-02, 2.9032e-02],
...,
[ 3.1938e-02, 3.2928e-02, 3.4993e-02, ..., -2.2898e-01,
-1.7910e-01, -6.1957e-02],
[-3.0038e-02, -8.1964e-02, -1.3208e-01, ..., 7.1000e-02,
1.1961e-01, 4.4919e-02],
[-3.6127e-03, 2.6732e-02, 7.4836e-02, ..., 2.2323e-02,
-1.8221e-02, -3.2994e-02]]],
[[[ 2.7723e-03, -8.2197e-03, 1.0577e-02, ..., -3.7032e-03,
7.6358e-03, -1.4880e-02],
[ 2.4787e-03, -2.0772e-02, 3.2836e-02, ..., 2.8386e-02,
-1.7299e-02, 5.8761e-03],
[ 6.0287e-03, -2.3994e-02, 3.9387e-02, ..., -3.5176e-02,
6.0395e-02, -2.4164e-02],
...,
[-9.6138e-03, 8.8951e-03, -9.7439e-02, ..., -4.7147e-01,
4.7197e-01, -2.1787e-01],
[-8.3267e-03, -6.4301e-03, -5.6976e-02, ..., -3.4977e-01,
3.3211e-01, -1.2867e-01],
[ 1.0966e-03, 1.4918e-03, -7.6965e-04, ..., -1.1620e-01,
1.1066e-01, -3.1141e-02]],
[[-4.6821e-03, -9.9369e-03, 6.2203e-04, ..., 1.0593e-02,
4.1388e-03, 1.0007e-02],
[ 1.5841e-02, -1.6072e-02, 5.1863e-02, ..., 4.3063e-02,
-2.2765e-02, 3.7528e-02],
[ 9.0599e-03, -2.7077e-02, 7.3893e-02, ..., -6.2686e-02,
8.0668e-02, -1.2081e-02],
...,
[-1.0123e-02, 1.6387e-02, -5.6721e-02, ..., -6.7181e-01,
6.2932e-01, -2.2616e-01],
[-7.2317e-03, 2.0273e-03, -1.1682e-02, ..., -4.9955e-01,
4.2043e-01, -1.0706e-01],
[-6.6669e-03, -1.6713e-02, 2.2226e-02, ..., -1.7237e-01,
1.0301e-01, 1.3447e-02]],
[[-7.5137e-03, 1.5078e-02, -4.5231e-04, ..., -4.4472e-03,
1.7667e-02, -7.8733e-04],
[ 2.1298e-03, -5.7614e-03, 1.3461e-02, ..., 8.3951e-03,
-2.2522e-03, 3.3652e-03],
[-9.2634e-04, -6.7298e-03, 3.0681e-02, ..., -2.8101e-02,
2.8818e-02, -5.8350e-04],
...,
[-1.0973e-02, 4.3662e-02, -1.0297e-01, ..., -3.7473e-01,
3.5543e-01, -1.4954e-01],
[-7.0714e-03, 2.3774e-02, -7.7243e-02, ..., -2.9655e-01,
2.5677e-01, -1.0678e-01],
[ 7.1839e-03, 1.4687e-02, -1.7310e-02, ..., -1.0573e-01,
7.8724e-02, -2.4353e-02]]],
...,
[[[-7.3473e-03, -3.2642e-02, 3.7394e-02, ..., -3.0790e-02,
-9.3275e-02, -6.0106e-02],
[-3.0986e-02, -6.0509e-02, 8.3517e-02, ..., 8.8217e-02,
-7.0693e-02, -1.0695e-01],
[-6.0400e-02, -1.4670e-01, 4.8319e-02, ..., 1.9756e-01,
2.1106e-02, -8.9696e-02],
...,
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Loading the checkpoint¶
At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network.
In [24]:
# TODO: Write a function that loads a checkpoint and rebuilds the model
def load_model(path):
checkpoint = torch.load(path)
structure = checkpoint['structure']
loaded_model = get_model(structure)
loaded_model.class_to_idx = checkpoint['class_to_idx']
loaded_model.load_state_dict(checkpoint['state_dict'])
loaded_model.classifier = checkpoint['classifier']
optimizer = checkpoint['optimizer']
epochs = checkpoint['epochs']
for param in model.parameters():
param.requires_grad = False
return model, optimizer, epochs
loaded_model, loaded_optimizer, epochs = load_model('checkpoint.pth')
#print(loaded_model)
print(loaded_model.state_dict())
/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.
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Inference for classification¶
Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called predict that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like
probs, classes = predict(image_path, model)
print(probs)
print(classes)
> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]
> ['70', '3', '45', '62', '55']
First you'll need to handle processing the input image such that it can be used in your network.
Image Preprocessing¶
You'll want to use PIL to load the image (documentation). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training.
First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the thumbnail or resize methods. Then you'll need to crop out the center 224x224 portion of the image.
Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so np_image = np.array(pil_image).
As before, the network expects the images to be normalized in a specific way. For the means, it's [0.485, 0.456, 0.406] and for the standard deviations [0.229, 0.224, 0.225]. You'll want to subtract the means from each color channel, then divide by the standard deviation.
And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using ndarray.transpose. The color channel needs to be first and retain the order of the other two dimensions.
In [25]:
def process_image(image):
''' Scales, crops, and normalizes a PIL image for a PyTorch model,
returns an Numpy array
'''
# TODO: Process a PIL image for use in a PyTorch model
img_pil = Image.open(image)
adjustments = transforms.Compose([
transforms.Resize(RESIZE),
transforms.CenterCrop(CROP_SIZE),
transforms.ToTensor(),
transforms.Normalize(mean=MEANS, std=ST_DEV)
])
img_tensor = adjustments(img_pil)
return img_tensor
img = (data_dir + '/test' + '/1/' + 'image_06743.jpg')
img = process_image(img)
print(img.shape)
torch.Size([3, 224, 224])
To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your process_image function works, running the output through this function should return the original image (except for the cropped out portions).
In [26]:
def imshow(image, ax=None, title=None):
"""Imshow for Tensor."""
if ax is None:
fig, ax = plt.subplots()
# PyTorch tensors assume the color channel is the first dimension
# but matplotlib assumes is the third dimension
image = image.numpy().transpose((1, 2, 0))
# Undo preprocessing
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image = std * image + mean
# Image needs to be clipped between 0 and 1 or it looks like noise when displayed
image = np.clip(image, 0, 1)
ax.imshow(image)
return ax
In [27]:
imshow(process_image("flowers/test/1/image_06743.jpg"))
Out[27]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f717bcba128>
Class Prediction¶
Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.
To get the top $K$ largest values in a tensor use x.topk(k). This method returns both the highest k probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using class_to_idx which hopefully you added to the model or from an ImageFolder you used to load the data (see here). Make sure to invert the dictionary so you get a mapping from index to class as well.
Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.
probs, classes = predict(image_path, model)
print(probs)
print(classes)
> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]
> ['70', '3', '45', '62', '55']
In [28]:
def predict(image_path, model, topk=5):
''' Predict the class (or classes) of an image using a trained deep learning model.
'''
# TODO: Implement the code to predict the class from an image file
model.to('cuda:0')
img_tensor = process_image(image_path)
img_tensor = img_tensor.unsqueeze_(0)
img_tensor = img_tensor.float()
with torch.no_grad():
output = model.forward(img_tensor.cuda())
probability = F.softmax(output.data, dim=1)
return probability.topk(topk)
In [29]:
img = (data_dir + '/test' + '/2/' + 'image_05100.jpg')
val1, val2 = predict(img, model)
print(val1)
print(val2)
tensor([[ 0.9798, 0.0150, 0.0017, 0.0009, 0.0005]], device='cuda:0') tensor([[ 14, 37, 77, 42, 50]], device='cuda:0')
In [30]:
img = (data_dir + '/test' + '/2/' + 'image_05100.jpg')
val1, val2 = predict(img, loaded_model)
print(val1)
print(val2)
tensor([[ 0.9798, 0.0150, 0.0017, 0.0009, 0.0005]], device='cuda:0') tensor([[ 14, 37, 77, 42, 50]], device='cuda:0')
In [31]:
img = (data_dir + '/test' + '/2/' + 'image_05107.jpg')
val1, val2 = predict(img, model)
print(val1)
print(val2)
tensor([[ 0.9611, 0.0213, 0.0063, 0.0031, 0.0017]], device='cuda:0') tensor([[ 14, 69, 68, 78, 42]], device='cuda:0')
In [32]:
img = (data_dir + '/test' + '/2/' + 'image_05107.jpg')
val1, val2 = predict(img, loaded_model)
print(val1)
print(val2)
tensor([[ 0.9611, 0.0213, 0.0063, 0.0031, 0.0017]], device='cuda:0') tensor([[ 14, 69, 68, 78, 42]], device='cuda:0')
Sanity Checking¶
Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use matplotlib to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:
You can convert from the class integer encoding to actual flower names with the cat_to_name.json file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the imshow function defined above.
In [59]:
# TODO: Display an image along with the top 5 classes
def sanity_check(model, path):
probabilities = predict(path, model)
image = Image.open(path)
img_filename = path.split('/')[-2]
fig, (ax1, ax2) = plt.subplots(figsize=(6,10), ncols=1, nrows=2)
ax1.set_title(cat_to_name[img_filename])
ax1.imshow(image)
ax1.axis('off')
y_pos = np.arange(len(probabilities))
a = np.array(probabilities[0][0])
names = [cat_to_name[str(index + 1)] for index in np.array(probabilities[1][0])]
N=float(len(names))
tickLocations = np.arange(N)
ax2.bar(tickLocations, a, align='center')
ax2.set_title('Class Probability')
ax2.set_xticks(ticks = tickLocations)
ax2.set_xticklabels(names)
ax2.set_xlim(min(tickLocations)-0.6,max(tickLocations)+0.6)
ax2.set_yticks([0.2,0.4,0.6,0.8,1,1.2])
ax2.set_ylim((0,1))
image_path = test_dir + '/2/image_05100.jpg'
sanity_check(loaded_model, image_path)
In [60]:
image_path = test_dir + '/2/image_05107.jpg'
sanity_check(loaded_model, image_path)
In [ ]: